Builds a risk prediction signature for a specific stratum by down-weighting the observations from the other strata using a range of weights. CoxBoost actively controls the extent to which each stratum contributes to the variable selection and estimation of regression coefficients. It also focuses on building a risk prediction signature for a specific stratum by down-weighting the observations from the other strata using a range of weights. CoxBoost was designed to identify clusters of variables that either are important only in the stratum of interest or are also important to some extent in the other strata.

CoxBoost statistics

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CoxBoost in publications

[…] likelihood-based boosting for generalized linear and additive regression models is provided by the r add-on package gamboost [], and an adapted version for boosting cox regression is provided with coxboost []. for a comparison of both statistical boosting approaches, that is, likelihood-based and gradient boosting in case of cox proportional hazard models, we refer to []., statistical boosting […]

[…] in the glmnet package [] which also can be combined with stability selection via stabs. note that also other implementations for boosting survival models are available in the r framework (gbm [], coxboost []) as well as methods depending on the brier score [], like the peperr [] and the pec [] packages., we carried out a simulation study to check the performance of stability selection […]

[…] the average of the corresponding chf of the leaf node of each tree. [], this was proposed in [–] to estimate parameter vector (β) in the cox proportional hazards model. in each boosting step, the coxboost adaptively selects a flexible subset of covariates to update the corresponding parameters. in the kth boosting step, the newton-raphson step will be separately used for gk predetermined […]

[…] applied to reduce the set of predictive genes did not take into account the correlation between genes. in this paper, we studied the performances of three high-dimensional regression methods – coxboost, lasso (least absolute shrinkage and selection operator), and elastic net – to identify prognostic signatures in patients with early breast cancer., we analyzed three public retrospective […]

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